Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Deep Recurrent Optimal Stopping
Authors: Niranjan Damera Venkata, Chiranjib Bhattacharyya
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Experiments We compare our OSPG algorithm using deep neural networks (DNN-OSPG) and recurrent neural networks (RNN-OSPG) against the following model-free discrete-time optimal stopping approaches. |
| Researcher Affiliation | Collaboration | Niranjan Damera Venkata Digital and Transformation Organization HP Inc., Chennai, India EMAIL Chiranjib Bhattacharyya Dept. of CSA and RBCCPS Indian Institute of Science, Bangalore, India EMAIL |
| Pseudocode | Yes | Algorithm 1 Pseudocode for mini-batch computation of our temporal OSPG loss |
| Open Source Code | No | The paper does not provide any explicit statements or links indicating that the source code for the methodology described is publicly available. |
| Open Datasets | Yes | We select 17 multi-class time-series classification datasets from the UCR time-series repository [11] (see Appendix D for details of the datasets and selection process). |
| Dataset Splits | Yes | We train models on ten random 50% train-test splits, holding 20% of training data as a validation dataset. |
| Hardware Specification | Yes | All experiments were performed on a shared server configured with 2 Intel Xeon Silver 12core, 2.10 GHz CPUs with 256GB RAM and equipped with 6 NVIDIA 2080Ti GPUs. However, experiments were run on a single GPU at a time and no computation was distributed across GPUs. |
| Software Dependencies | No | The paper mentions software like Keras and Adam but does not specify their version numbers or the version of Python used, which are necessary for reproducible software dependencies. |
| Experiment Setup | Yes | Table 2 shows general hyper-parameter settings used for all experiments. |